Presentation
E-DGCN: An Efficient Architecture Design for Accelerating Dynamic Graph Convolutional Network (DGCN) Inference
DescriptionDynamic graph neural networks (DGCNs) have been proposed to extend machine learning techniques to applications involving dynamic graphs. Typically, a DGCN model includes a graph convolutional network (GCN) followed by a recurrent neural network (RNN) to capture both spatial and temporal information. To efficiently perform distinct neural network models as well as maximize the data reuse and hardware utilization, customized hardware designs for such applications require a reconfigurable computing engine, flexible dataflow, and efficient data locality exploitation. We propose an efficient DGCN accelerator named E-DGCN. Specifically, E-DGCN includes modified Processing Elements (PEs) with a flexible interconnection design to support diverse computation patterns and various dataflows. Additionally, a lightweight vertex caching algorithm is proposed to exploit data locality, enabling E-DGCN to selectively load required vertices during DGCN inference. These implementations provide benefits in managing data computation and communication.
Event Type
Research Manuscript
TimeThursday, June 2710:30am - 10:45am PDT
Location3010, 3rd Floor
AI
Design
AI/ML Architecture Design